Topological MRI Prostate Segmentation Method

The main aim of this paper is to advance the state of the art in automated prostate segmentation using T2 weighted MR images, by introducing a hybrid topological MRI prostate segmentation method which is based on a set of pre-labeled MR atlas images. The proposed method has been experimentally tested on a set of 30 MRI T2 weighted images. For evaluation the automated segmentations of the proposed scheme have been compared with the manual segmentations, using an average Dice Similarity Coefficient (DSC). Obtained quantitative results have shown a good approximation of the segmented prostate

Automatic Optimum Atlas Selection for Multi-Atlas Image Segmentation using Joint Label Fusion

Multi-atlas image segmentation using label fusion is one of the most accurate state of the art image segmentation techniques available for biomedical imaging applications. Motivated to achieve higher image segmentation accuracy, reduce computational costs and a continuously increasing atlas data size, a robust framework for optimum selection of atlases for label fusion is vital. Although believed not to be critical for weighted label fusion techniques by some works (Sabuncu, M. R. et al., 2010, [1]), others have shown that appropriate atlas selection has several merits and can improve multi-atlas image segmentation accuracy (Aljabar et al., 2009, [2], Van de Velde et al., 2016) [27]. This thesis proposed an automatic Optimum Atlas Selection (OAS) framework pre-label fusion step that improved image segmentation performance dice similarity scores using Joint Label Fusion (JLF) implementation by Wang et al, 2013, [3, 26]. A selection criterion based on a global majority voting fusion output image similarity comparison score was employed to select an optimum number of atlases out of all available atlases to perform the label fusion step. The OAS framework led to observed significant improvement in aphasia stroke heads magnetic resonance (MR) images segmentation accuracy in leave-one out validation tests by 1.79% (p = 0.005520) and 0.5% (p = 0.000656) utilizing a set of 7 homogenous stroke and 19 inhomogeneous atlas datasets respectively. Further, using comparatively limited atlas data size (19 atlases) composed of normal and stroke head MR images, t-tests showed no statistical significant difference in image segmentation performance dice scores using the proposed OAS protocol compared to using known automatic Statistical Parametric Mapping (SPM) plus a touchup algorithm protocol [4] for image segmentation (p = 0.49417). Thus, leading to the conclusions that the proposed OAS framework is an effective and suitable atlas selection protocol for multi-atlas image segmentation that improves brain MR image segmentation accuracy. It is comparably in performance to known image segmentation algorithms and can lead to reduced computation costs in large atlas data sets. With regards to future work, efforts to increase atlas data size and use of a more robust approach for determining the optimum selection threshold value and corresponding number of atlases to perform label fusion process can be explored to enhance overall image segmentation accuracy. Furthermore, for an unbiased performance comparison of the proposed OAS framework to other image segmentation algorithms, truly manually segmented atlas ground truth MR images and labels are needed

Multi-Atlas based Segmentation of Multi-Modal Brain Images

Brain image analysis is playing a fundamental role in clinical and population-based epidemiological studies. Several brain disorder studies involve quantitative interpretation of brain scans and particularly require accurate measurement and delineation of tissue volumes in the scans. Automatic segmentation methods have been proposed to provide reliability and accuracy of the labelling as well as performing an automated procedure. Taking advantage of prior information about the brain's anatomy provided by an atlas as a reference model can help simplify the labelling process. The segmentation in the atlas-based approach will be problematic if the atlas and the target image are not accurately aligned, or if the atlas does not appropriately represent the anatomical structure/region. The accuracy of the segmentation can be improved by utilising a group of atlases. Employing multiple atlases brings about considerable issues in segmenting a new subject's brain image. Registering multiple atlases to the target scan and fusing labels from registered atlases, for a population obtained from different modalities, are challenging tasks: image-intensity comparisons may no longer be valid, since image brightness can have highly diff ering meanings in dfferent modalities. The focus is on the problem of multi-modality and methods are designed and developed to deal with this issue specifically in image registration and label fusion. To deal with multi-modal image registration, two independent approaches are followed. First, a similarity measure is proposed based upon comparing the self-similarity of each of the images to be aligned. Second, two methods are proposed to reduce the multi-modal problem to a mono-modal one by constructing representations not relying on the image intensities. Structural representations work on the basis of using un-decimated complex wavelet representation in one method, and modified approach using entropy in the other one. To handle the cross-modality label fusion, a method is proposed to weight atlases based on atlas-target similarity. The atlas-target similarity is measured by scale-based comparison taking advantage of structural features captured from un-decimated complex wavelet coefficients. The proposed methods are assessed using the simulated and real brain data from computed tomography images and different modes of magnetic resonance images. Experimental results reflect the superiority of the proposed methods over the classical and state-of-the art methods

A Semi-Automated Approach to Medical Image Segmentation using Conditional Random Field Inference

Medical image segmentation plays a crucial role in delivering effective patient care in various diagnostic and treatment modalities. Manual delineation of target volumes and all critical structures is a very tedious and highly time-consuming process and introduce uncertainties of treatment outcomes of patients. Fully automatic methods holds great promise for reducing cost and time, while at the same time improving accuracy and eliminating expert variability, yet there are still great challenges. Legally and ethically, human oversight must be integrated with ”smart tools” favoring a semi-automatic technique which can leverage the best aspects of both human and computer. In this work we show that we can formulate a semi-automatic framework for the segmentation problem by formulating it as an energy minimization problem in Conditional Random Field (CRF). We show that human input can be used as adaptive training data to condition a probabilistic boundary term modeled for the heterogeneous boundary characteristics of anatomical structures. We demonstrated that our method can effortlessly adapt to multiple structures and image modalities using a single CRF framework and tools to learn probabilistic terms interactively. To tackle a more difficult multi-class segmentation problem, we developed a new ensemble one-vs-rest graph cut algorithm. Each graph in the ensemble performs a simple and efficient bi-class (a target class vs the rest of the classes) segmentation. The final segmentation is obtained by majority vote. Our algorithm is both faster and more accurate when compared with the prior multi-class method which iteratively swaps classes. In this Thesis, we also include novel volumetric segmentation algorithms which employ deep learning and indicate how to synthesize our CRF framework with convolutional neural networks (CNN). This would allow incorporating user guidance into CNN based deep learning for this task. We think a deep learning based method interactively guided by human expert is the ideal solution for medical image segmentation